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Creators/Authors contains: "Watanuki, Hugo"

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  1. ; ; ; ; ; (, IEEE)
    Attackers are increasingly using model inversion attacks, in which the outputs of the model can be used to reconstruct confidential or private information to target machine learning models, especially those that handle sensitive financial data. We propose an attack model that exploits the output of classification models to infer details about the training data. We implement our experiments on the HPCC Systems platform. HPCC Systems is known for its robust data processing capabilities. Our approach systematically exploits the output of financial data-based classification models to reconstruct sensitive attributes, thereby demonstrating the potential risks and vulnerabilities resulting from an attack. In our research, we also have tested some defensive strategies to secure the model against inversion attack. 
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    Free, publicly-accessible full text available February 5, 2026